Intra- and inter-species interactions drive early phases of invasion in mice gut microbiota
Bibliographic record
Abstract
Abstract The stability and dynamics of ecological communities are dictated by interaction networks typically quantified at the level of species. 1–10 But how such networks are influenced by intra-species variation (ISV) is poorly understood. 11–14 Here, we use ~500,000 chromosomal barcodes to track high-resolution intra-species clonal lineages of Escherichia coli invading mice gut with the increasing complexity of gut microbiome: germ-free, antibiotic-perturbed, and innate microbiota. By co-clustering the dynamics of intra-species clonal lineages and those of gut bacteria from 16S rRNA profiling, we show the emergence of complex time-dependent interactions between E. coli clones and resident gut bacteria. With a new approach, dynamic covariance mapping (DCM), we differentiate three phases of invasion in susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli coexisting with resident bacteria in a quasi-steady state. Comparison of the dynamics, stability and fitness from experimental replicates and different cohorts suggest that phase 1 is driven by mutations in E. coli before colonization, while phase 3 is by de novo mutations. Our results highlight the transient nature of interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. One-Sentence Summary High-resolution lineage tracking and dynamic covariance mapping (DCM) define three distinct phases during early gut microbiome invasion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".